About

Professor Gregory Cogan leads The Cogan Lab at Duke University, which focuses on the cognitive neuroscience of speech. The lab's research centers on studying speech, language, and cognition through the use of invasive electrophysiological recordings, including electrocorticography (ECoG), stereoelectroencephalography (SEEG), and micro-electrocorticography (µECoG). This approach allows for detailed investigation of the neural mechanisms underlying speech and language processing. The lab's work contributes to advancing the understanding of how the brain supports complex cognitive functions related to communication.

Research topics

• Artificial Intelligence
• Computer Science
• Telecommunications
• Machine Learning
• Nanotechnology
• Optoelectronics
• Biomedical engineering
• Materials science
• Speech recognition
• Mathematics
• Statistics
• Neuroscience

Selected publications

• Review for "Modulation of Auditory Novelty Processing by Dexmedetomidine and Natural Sleep: A Human Intracranial Electrophysiology Study"

  2025-06-20

  peer-review1st authorCorresponding
  PublisherDOI

• Shared latent representations of speech production for cross-patient speech decoding

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-21 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved accuracies relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.

  PublisherOA PDFDOI

• Review for "Modulation of Auditory Novelty Processing by Dexmedetomidine and Natural Sleep: A Human Intracranial Electrophysiology Study"

  2025-03-24

  peer-review1st authorCorresponding
  PublisherDOI

• A Phase 1 Assessment of the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of (2<i>R</i>,6<i>R</i>)‐Hydroxynorketamine in Healthy Volunteers

  Clinical Pharmacology & Therapeutics · 2024-07-25 · 33 citations

  articleOpen access

  (R,S)-Ketamine (ketamine) is a dissociative anesthetic that also possesses analgesic and antidepressant activity. Undesirable dissociative side effects and misuse potential limit expanded use of ketamine in several mental health disorders despite promising clinical activity and intensifying medical need. (2R,6R)-Hydroxynorketamine (RR-HNK) is a metabolite of ketamine that lacks anesthetic and dissociative activity but maintains antidepressant and analgesic activity in multiple preclinical models. To enable future assessments in selected human indications, we report the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of RR-HNK in a Phase 1 study in healthy volunteers (NCT04711005). A six-level single-ascending dose (SAD) (0.1-4 mg/kg) and a two-level multiple ascending dose (MAD) (1 and 2 mg/kg) study was performed using a 40-minute IV administration emulating the common practice for ketamine administration for depression. Safety assessments showed RR-HNK possessed a minimal adverse event profile and no serious adverse events at all doses examined. Evaluations of dissociation and sedation demonstrated that RR-HNK did not possess anesthetic or dissociative characteristics in the doses examined. RR-HNK PK parameters were measured in both the SAD and MAD studies and exhibited dose-proportional increases in exposure. Quantitative electroencephalography (EEG) measurements collected as a PD parameter based on preclinical findings and ketamine's established effect on gamma-power oscillations demonstrated increases of gamma power in some participants at the lower/mid-range doses examined. Cerebrospinal fluid examination confirmed RR-HNK exposure within the central nervous system (CNS). Collectively, these data demonstrate RR-HNK is well tolerated with an acceptable PK profile and promising PD outcomes to support the progression into Phase 2.

  PublisherOA PDFDOI

• Distinct neural processes link speech planning and execution

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-10-07 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Speaking is the primary way that humans communicate. This communication is enabled by a production system that can plan and execute unique combinations of speech sounds. Although a distributed network of brain regions has been implicated in speaking, it is unclear how planning and execution of speech are coordinated to produce meaningful sounds. Leveraging the high spatio-temporal resolution of intracranial recordings at different spatial scales, we show distinct neural mechanisms that facilitate speech planning and execution. During planning, different levels of speech units are coded discretely at distinct prefrontal sites. These planned units are then dynamically integrated at various cortical levels to guide subsequent execution. During speech execution, speech motor regions generate continuous sequences that reflect both discrete speech sound units and their transitional properties between units. This rapid neural transition from discrete speech units to motor sequences links speech planning with execution and enables our effortless ability to speak.

  PublisherOA PDFDOI

• Flexible, high‐resolution cortical arrays with large coverage capture microscale high‐frequency oscillations in patients with epilepsy

  Epilepsia · 2023-05-08 · 23 citations

  articleOpen access

  OBJECTIVE: Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. METHODS: We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. RESULTS: We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. SIGNIFICANCE: These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.

  PublisherOA PDFDOI

• High-resolution neural recordings improve the accuracy of speech decoding

  Nature Communications · 2023 · 67 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

  PublisherOA PDFDOI

• High-resolution neural recordings improve the accuracy of speech decoding

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-05-20 · 3 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One promising solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed novel, high-resolution, micro-electrocorticographic (μECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to standard invasive recordings. This increased signal quality improved phoneme decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show for the first time that μECoG can enable high-quality speech decoding, demonstrating its ability to improve neural interfaces for neural speech prostheses.

  PublisherOA PDFDOI

• Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array

  Brain Communications · 2022-05-02 · 26 citations

  articleOpen access

  Abstract One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30–70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100–1000 mm2 area to enable high-resolution mapping of neural activity. Here, we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings in nine patients with epilepsy. In addition, we recorded from four patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Recordings in epileptic patients had a significantly higher microseizure rate (2.01 events/min) than recordings in non-epileptic subjects (0.01 events/min; permutation test, P = 0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

  PublisherOA PDFDOI

• Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array

  medRxiv · 2021-09-15 · 4 citations

  preprintOpen access

  Abstract One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30–70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100–1,000 mm 2 area to enable high-resolution mapping of neural activity. Here we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings within epileptic cortex (the site of seizure onset and early spread) in nine patients with epilepsy. In two of these patients, we obtained recordings from cortical areas distal to the epileptic cortex. Additionally, we recorded from two non-epileptic patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic tissue controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Epileptic cortex exhibited a significantly higher microseizure rate (2.01 events/min) than non-epileptic cortex (0.01 events/min; permutation test, P =0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

  PublisherOA PDFDOI

Frequent coauthors

• David Poeppel

  New York University

  9 shared

• Shervin Rahimpour

  9 shared

• Jonathan Viventi

  Duke University

  8 shared

• Chia‐Han Chiang

  Duke University

  8 shared

• Orrin Devinsky

  Toronto Western Hospital

  8 shared

• Katrina Barth

  Duke University

  7 shared

• Bijan Pesaran

  University of Pennsylvania

  7 shared

• Derek G. Southwell

  Duke University

  7 shared

Awards & honors

• Duke Scientists Create Brain Implant That May Enable Communi…
• How Duke Researchers Defend the Brain (2023)
• Researchers take another step closer to mind-reading compute…
• MEDx connects School of Medicine and Pratt to encourage rese…
• Researchers' discovery of new verbal working memory architec…